# Compute distance matrices
print_heading('CALCULATING DISTANCE MATRICES')
distances = {}
analysis_times = []
timer = Timer()
timer.start()
for ID in experiment_bouts.metadata['ID'].unique():
output_path, path_exists = create_filepath(output_directory, ID, '.npy', True)
if path_exists:
distances[ID] = np.load(output_path)
if not path_exists:
print ID + '...',
queries = experiment_bouts.list_bouts(IDs=[ID], values=True, ndims=n_dims)
fish_distances = calculate_distance_matrix_templates(queries, exemplars, fs=frame_rate)
distances[ID] = fish_distances
time_taken = timer.lap()
analysis_times.append(time_taken)
print timer.convert_time(time_taken)
np.save(output_path, fish_distances)
if len(analysis_times) > 0:
print 'Average time: {}'.format(timer.convert_time(timer.average))
# Assign exemplars
mapped_bouts = experiment_bouts.metadata.copy()
mapped_bouts['exemplar'] = None
for ID, fish_distances in distances.iteritems():
bout_idxs = mapped_bouts[mapped_bouts['ID'] == ID].index
nearest_exemplar = np.argmin(fish_distances, axis=1)
mapped_bouts.loc[bout_idxs, 'exemplar'] = nearest_exemplar
mapped_bouts.to_csv(os.path.join(experiment.subdirs['analysis'], 'mapped_bouts.csv'))
timer = Timer()
timer.start()
# Compute the average of 100 shuffled transition matrices for each fish
print 'Generating shuffled matrices...',
n_shuffles = 100
S = np.zeros((n_fish, len(isomap), len(isomap)))
for shuffle in range(n_shuffles):
S += fish_transition_matrices(bouts, shuffle=True, verbose=False)
S /= n_shuffles
S = redistribute_transitions(S, weights)
np.save(
os.path.join(transition_directory, 'shuffled_transition_matrices.npy'),
S)
print timer.convert_time(timer.lap())
S = np.load(
os.path.join(transition_directory, 'shuffled_transition_matrices.npy'))
T = np.load(
os.path.join(transition_directory, 'smoothed_transition_matrices.npy'))
# Generate permuted matrices
print 'Generating permutations...',
S_permuted = np.empty((n_permutations, S.shape[1], S.shape[2]))
T_permuted = np.empty((n_permutations, T.shape[1], T.shape[2]))
for permutation in range(n_permutations):
shuffled_idxs = np.random.permutation(
np.arange(n_fish))[:(n_fish + 1) / 2]
S_permuted[permutation] = S[shuffled_idxs].sum(axis=0)
T_permuted[permutation] = T[shuffled_idxs].sum(axis=0)